Summary

Model v0.2.3 was created using wind, lag_sst, int_chl, sss for cfin. The models were used to project the probability of the study area of having a cfin abundance of over 10^{4} per \(m^3\), which is the right whale feeding threshold selected for this model. The models were then built using the species distribution modeling package, Biomod2, which builds presence-absence models using any of 10 different algorithms. The algorithms selected were generalized additive models (GAMs), good explanatory models, boosted regression trees (BRTs), good predictive models, and random forests (RFs), highly accurate predictive models. One model was built for each month, and then projected back onto the environmental data from that month for every year between 2000 and 2017.

The models were averaged into climatologies with one climatology per month. Evaluations were compiled for each indiviual year and plotted by month. Finally, the study area was divided up into three regions, the Mid-Atlantic Bight (MAB), George’s Bank (GBK), and the Gulf of Maine (GOM). Actual versus predicted abundance values were plotted for each region. The models were produced using the ECOMON dataset(s).

Climatologies

Ensemble Climatology

The ensemble models were created using the biomod2 package in R. The ensembles consist of BRTs, GAMs, and RFs. The ensembles were used to model the right whale feeding threshold, with any abundance greater than 10^{4} cfin per \(m^3\) counted as a presence and anything below that threshold counted as an absence.

Figure 1. Monthly climatological ensemble projections of GAMs, BRTs, and random forests (RFs). The climatology was created by averaging together the projections from 2000 to 2017.

GAM Climatology

The GAM models created with biomod2 were used to model the right whale feeding threshold, with any abundance greater than 10^{4} cfin per \(m^3\) counted as a presence and anything below that threshold counted as an absence.

Figure 2. Monthly climatological GAM projections produced using Biomod2. The climatology was created by averaging together the projections from 2000 to 2017.

BRT Climatology

The BRT models created with biomod2 were used to model the right whale feeding threshold, with any abundance greater than 10^{4} cfin per \(m^3\) counted as a presence and anything below that threshold counted as an absence.

Figure 3. Monthly climatological BRT projections produced using Biomod2. The climatology was created by averaging together the projections from 2000 to 2017.

RF Climatology

The RF models created with biomod2 were used to model the right whale feeding threshold, with any abundance greater than 10^{4} cfin per \(m^3\) counted as a presence and anything below that threshold counted as an absence.

Figure 4. Monthly climatological RF projections produced using Biomod2. The climatology was created by averaging together the projections from 2000 to 2017.

Monthly ensemble projections

Monthly ensemble Biomod2 projections are displayed below for the months of May, June, July, August, and September.

April

Figure 5. Ensemble projections for the month of April from 2000 to 2017.

May

Figure 6. Ensemble projections for the month of May from 2000 to 2017.

June

Figure 7. Ensemble projections for the month of June from 2000 to 2017`.

August

Figure 8. Ensemble projections for the month of August from 2000 to 2017.

September

Figure 9. Ensemble projections for the month of September from 2000 to 2017.

Evaluations

Evaluation metrics were selected based on availability within the Biomod2 package. The area under the receiver operator characteristic curve (AUC) and the true skill statistic (TSS) were computed during the creation of the model object.

Ensemble evaluations

Figure 10. Biomod ensemble evaluations on a monthly time scale using a.) AUC and b.) TSS

GAM evaluations

Figure 11. Biomod GAM evaluations on a monthly time scale using a.) AUC and b.) TSS

BRT evaluations

Figure 12. Biomod BRT evaluations on a monthly time scale using a.) AUC and b.) TSS

RF evaluations

Figure 13. Biomod RF evaluations on a monthly time scale using a.) AUC and b.) TSS

Variable contribution

Variable contribution was saved during each model run and then reloaded and plotted on a monthly basis and standardized so the total contribution is equal to 100%. This was only done for the individual models.

GAM variable contribution

Figure 14. Biomod GAM variable contributions on a monthly time scale.

BRT variable contribution

Figure 15. Biomod BRT variable contributions on a monthly time scale.

RF variable contribution

Figure 16. Biomod RF variable contributions on a monthly time scale.

Climatological and inter-annual actual abundance vs. predicted

For each model, the logged actual abundance of cfin was plotted against the predicted probability of suitability. Error bars indicate the variance. The plots are color coded by region, either Mid-Atlantic Bight (MAB), George’s Bank (GBK), or the Gulf of Maine (GOM).

Ensemble model

Figure 17. Actual logged abundance versus predicted probability of suitability for cfin for a.) all 12 months and b.) all years.

GAM model

Figure 18. Actual logged abundance versus predicted probability of suitability for cfin for a.) all 12 months and b.) all years.

BRT model

Figure 19. Actual logged abundance versus predicted probability of suitability for cfin for a.) all 12 months and b.) all years.

RF model

Figure 20. Actual logged abundance versus predicted probability of suitability for cfin for a.) all 12 months and b.) all years.

Region plots

For each region, a plot was created comparing the actual abundance of cfin to the predicted probability of habitat suitability. The shaded confidence intereval represents variance.

Ensemble region plots

Figure 21. Plots of actual vs. predicted abundance in different regions.

GAM region plots

Figure 22. Plots of actual vs. predicted abundance in different regions.

BRT region plots

Figure 23. Plots of actual vs. predicted abundance in different regions.

RF region plots

Figure 24. Plots of actual vs. predicted abundance in different regions.